Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "117" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 19 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.898951 | 15.366967 | 12.770016 | 13.692653 | 7.171720 | 9.200010 | 0.747919 | 2.635189 | 0.0280 | 0.0314 | 0.0027 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.151468 | 15.614565 | 11.879941 | 12.896401 | 7.800020 | 9.361296 | 0.696252 | 2.512051 | 0.0294 | 0.0352 | 0.0037 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.628822 | 15.109225 | 10.263202 | 11.304263 | 7.671774 | 9.501037 | 1.880654 | 3.785806 | 0.0276 | 0.0313 | 0.0025 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.771340 | 17.552946 | 10.116623 | 11.102604 | 9.029371 | 10.699603 | 0.855934 | 2.720751 | 0.0285 | 0.0311 | 0.0021 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.119794 | 14.475048 | 9.910452 | 10.792022 | 8.938489 | 10.980646 | 0.704224 | 2.494951 | 0.0286 | 0.0332 | 0.0031 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.897964 | 14.682864 | 8.820072 | 9.858859 | 7.873935 | 9.201353 | 0.163080 | 1.735278 | 0.0279 | 0.0309 | 0.0022 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.055716 | 17.149717 | 10.221067 | 11.096716 | 10.640672 | 13.156801 | 0.632756 | 2.387321 | 0.0278 | 0.0304 | 0.0020 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.860095 | 14.482356 | 9.901541 | 10.934780 | 6.299948 | 7.935818 | 2.395676 | 5.432501 | 0.0282 | 0.0330 | 0.0032 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.515598 | 17.630434 | 10.847468 | 11.804913 | 9.226816 | 11.187635 | 5.561313 | 10.537254 | 0.0280 | 0.0317 | 0.0027 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.458567 | 16.002827 | 10.043809 | 10.990208 | 7.103616 | 8.548919 | 1.451402 | 4.549099 | 0.0283 | 0.0315 | 0.0023 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.861993 | 15.406020 | 10.405421 | 11.377213 | 9.521369 | 12.196134 | 4.474443 | 6.378700 | 0.0285 | 0.0339 | 0.0036 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.602375 | 15.031687 | 9.984217 | 10.792402 | 9.156472 | 11.101273 | 3.206158 | 7.494642 | 0.0286 | 0.0328 | 0.0029 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.018962 | 12.279557 | 8.479909 | 9.215156 | 4.427759 | 5.270282 | 2.414890 | 3.301399 | 0.0281 | 0.0320 | 0.0028 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.785653 | 13.842500 | 10.654592 | 11.498216 | 10.284351 | 12.293216 | 0.467703 | 2.682105 | 0.0289 | 0.0339 | 0.0035 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.568630 | 13.397632 | 9.567837 | 10.498764 | 8.885952 | 10.751451 | 5.301257 | 5.736446 | 0.0283 | 0.0332 | 0.0033 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.980890 | 13.953821 | 8.875239 | 9.835986 | 8.852905 | 10.087037 | 2.098516 | 4.280702 | 0.0300 | 0.0319 | 0.0026 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.105001 | 14.127057 | 9.634654 | 10.586665 | 9.198008 | 10.912320 | 1.136164 | 3.507499 | 0.0274 | 0.0301 | 0.0021 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.407491 | 14.896903 | 9.451945 | 10.413655 | 9.153587 | 11.286300 | 1.224209 | 3.738479 | 0.0279 | 0.0339 | 0.0040 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.319017 | 14.403789 | 9.955673 | 10.863239 | 9.230706 | 10.758008 | 0.958889 | 2.578945 | 0.0277 | 0.0313 | 0.0027 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 15.366967 | 11.898951 | 15.366967 | 12.770016 | 13.692653 | 7.171720 | 9.200010 | 0.747919 | 2.635189 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 15.614565 | 12.151468 | 15.614565 | 11.879941 | 12.896401 | 7.800020 | 9.361296 | 0.696252 | 2.512051 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 15.109225 | 11.628822 | 15.109225 | 10.263202 | 11.304263 | 7.671774 | 9.501037 | 1.880654 | 3.785806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 17.552946 | 13.771340 | 17.552946 | 10.116623 | 11.102604 | 9.029371 | 10.699603 | 0.855934 | 2.720751 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.475048 | 14.475048 | 11.119794 | 10.792022 | 9.910452 | 10.980646 | 8.938489 | 2.494951 | 0.704224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.682864 | 14.682864 | 10.897964 | 9.858859 | 8.820072 | 9.201353 | 7.873935 | 1.735278 | 0.163080 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 17.149717 | 17.149717 | 13.055716 | 11.096716 | 10.221067 | 13.156801 | 10.640672 | 2.387321 | 0.632756 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.482356 | 10.860095 | 14.482356 | 9.901541 | 10.934780 | 6.299948 | 7.935818 | 2.395676 | 5.432501 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 17.630434 | 17.630434 | 13.515598 | 11.804913 | 10.847468 | 11.187635 | 9.226816 | 10.537254 | 5.561313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 16.002827 | 16.002827 | 12.458567 | 10.990208 | 10.043809 | 8.548919 | 7.103616 | 4.549099 | 1.451402 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 15.406020 | 11.861993 | 15.406020 | 10.405421 | 11.377213 | 9.521369 | 12.196134 | 4.474443 | 6.378700 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 15.031687 | 11.602375 | 15.031687 | 9.984217 | 10.792402 | 9.156472 | 11.101273 | 3.206158 | 7.494642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 12.279557 | 10.018962 | 12.279557 | 8.479909 | 9.215156 | 4.427759 | 5.270282 | 2.414890 | 3.301399 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 13.842500 | 13.842500 | 10.785653 | 11.498216 | 10.654592 | 12.293216 | 10.284351 | 2.682105 | 0.467703 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 13.397632 | 13.397632 | 10.568630 | 10.498764 | 9.567837 | 10.751451 | 8.885952 | 5.736446 | 5.301257 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 13.953821 | 10.980890 | 13.953821 | 8.875239 | 9.835986 | 8.852905 | 10.087037 | 2.098516 | 4.280702 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.127057 | 14.127057 | 11.105001 | 10.586665 | 9.634654 | 10.912320 | 9.198008 | 3.507499 | 1.136164 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.896903 | 11.407491 | 14.896903 | 9.451945 | 10.413655 | 9.153587 | 11.286300 | 1.224209 | 3.738479 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | N07 | RF_maintenance | nn Shape | 14.403789 | 14.403789 | 11.319017 | 10.863239 | 9.955673 | 10.758008 | 9.230706 | 2.578945 | 0.958889 |